Director of Security Engineering- SaaS Intelligence

You are ready to lead and influence the future of technology at a world-renowned company. Join JPMorganChase and unleash your potential in shaping the industry.

As a Director of Security Engineering at JPMorganChase within the Enterprise Technology Security — SaaS Intelligence, you lead at least one technical area with a security engineering focus, and drive impact within teams, technologies, and projects across departments. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a decision maker for your teams and a driver of innovation to deliver security solutions. You are a hands-on technical leader who develops engineers, drives delivery quality, and partners across the organization to scale the services, reach and impact.

Job responsibilities

• Influences peer leaders and senior stakeholders across the business, product, and technology teams

• Leads reuse-first adoption of enterprise-authorized AI capabilities within the work environment to accelerate security engineering decisioning and delivery across the area, with human-in-the-loop validation and appropriate handling of sensitive data.

• Delivers technical solutions that can be leveraged across multiple businesses/domains

• Owns engineering execution for SaaS application onboarding at scale — driving connector development, integration validation, pre-production testing rigor, and quality controls that prevent production incidents.

• Sets and enforces engineering standards for code quality, API design, integration testing, and pre-production validation; leads post-incident reviews and corrective action follow-through.

• Accountable for decisions that influence teams’ resources, budget, tactical operations and the execution and implementation of processes and procedures

• Manages delivery planning and execution for the team's sprint and quarterly commitments; provides clear status, risk identification, and escalation to senior leadership.

• Establishes governance standards for AI-assisted workflows used in security engineering delivery and decision-making, ensuring traceability/auditability and alignment to security, resiliency, and control expectations.

Required Qualifications Capabilities and Skills

• Formal training or certification on security engineering concepts and 10+ years applied experience (NAMR/APAC – India/ LATAM/ Hong Kong)

• Experience running teams of engineers that deliver security solutions operations on cloud-based platforms and applications and experience building security engineering products and solutions for public cloud-based applications and infrastructure

• Demonstrated experience leading teams in the safe use of enterprise-authorized AI capabilities within the work environment for security engineering workflows, including validation habits and awareness of data sensitivity.

• Ability to evaluate AI-assisted recommendations before adoption and set review/approval expectations that align to security, resiliency, and auditability outcomes.

• Demonstrated experience developing engineers through coaching, structured code review, and setting and holding delivery quality standards.

• Experience hiring, developing, and recognizing engineering talent across multiple levels.

Preferred qualifications, capabilities, and skills

• Hands-on experience with SaaS Security Posture Management capability, SaaS AI Observability, and SaaS Secure Service Access. platforms or tooling

• Experience with secrets management patterns and credential governance

• Familiarity with third-party risk governance frameworks and how they intersect with SaaS and vendor risk decisions.

• Experience building or operating services that have auth, authorization delegation, scoped access, or API gateway patterns within a regulated enterprise environment.

• Experience operating within a financial services or similarly regulated environment, including familiarity with findings management, audit observations, and control closure processes.